Devices for detecting direct mind-machine interaction (MMI) have been proposed and researched for many years. The most carefully controlled and best-explored experiments utilize some type of non-deterministic, or true, random number generator that produces a sequence of random numbers, usually in a binary form. The most common random number generators used are of the electronic type that produce a sequence of random binary bits.
In typical MMI experiments, a source of non-deterministic random numbers (SNDRN) is operated in conjunction with a human operator who attempts to influence the statistical properties of the SNDRN's output sequence. The operator, or subject, is directed to intend mentally the number of ones produced in the random sequence to be either higher, lower, or equal to the statistically expected number.
The results of these experiments, compiled over thousands of experimental trials, show a small but persistent and statistically significant effect. A most notable example of a research program for detecting MMI is the program known as Princeton Engineering Anomalies Research (PEAR) that ran for 27 years at Princeton University. This work is described in detail in the book Margins of Reality, the Role of Mind in the Physical World, by Robert Jahn and Brenda Dunne, Harcourt Brace and Company, 1987.
The PEAR lab and numerous other facilities around the world have established, to a very high level of statistical significance, the existence of a link between the mental intention of an operator and results of measurements of SNDRN output. Demonstrating the reality of MMI is of great scientific interest. Nevertheless, the laboratory demonstration did not immediately translate into useful devices or methods. Practical applications of MMI have not generally been achieved due to an absence of understanding of why or how the effect manifests, and because the experimental devices and data processing methods used were not sensitive enough to the effect.
Journal articles by many authors have suggested a variety of potential uses of MMI. These suggestions are made without disclosing means for their implementation. Apparatuses for experiments involving MMI have been complex and expensive. U.S. Pat. No. 5,830,064, issued Nov. 3, 1998, to Bradish et al, teaches a method and apparatus of generating values and detecting whether the values fall outside chance expectations. This patent involves converting some of the values according to a selection pattern in order to measure a collective statistical variance.
International Publication Number WO 2007/014031, published 1 Feb. 2007, teaches devices and methods for responding to influences of mind, including abstraction of patterns in numbers reflective of influences of mind.
During the last several decades, computer-related techniques involving artificial neural networks (ANNs) and artificial intelligence have been developed for such applications as data analysis, cognitive research and problem-solving. Conventional ANN and AC techniques are limited, however, because they are not responsive to an influence of mind.